When people ask me about my data analytics career, I’m very cautious and always insist on one thing:

Don’t use survivor bias as a template for your future life

I used to like to share my experience of changing industries with others, because I also changed industries and taught myself to do data analysis. Now I also do some work related to mathematical fractions, and I often interview new people in data analysis positions. I am also asked questions about changing industries on the official account every day, and I always share my story every time

But then I found out that I was wrong. Not only would it not give them a true reference, it would mislead them and give them the illusion that:

Data analysis industry is so simple, it seems THAT I can also change to learn!

There are even a lot of people who quit teaching themselves data analysis naked, and finally, a chicken feather

This is very terrible, because any change jobs, are likely to affect a person and the rest of the family life, I don’t have the ability, didn’t also the right to decide and change someone’s life, gradually no longer to share their stories, so I only speak oneself in this industry for several years, the fugitive dust in the eyes can see, although the cold, also true

About data industry positions

First, demand for data analyst positions has fallen, mainly for novice analysts, while high-end positions remain in short supply

As for the reasons, on the one hand, the threshold of data analysis is too low and too many people rush in at a time. The ability of data analysis is gradually transformed into the subsidiary ability of the business side, and data analysis becomes worthless.

Second, data analysis is also the concept of people stir up, when the company boss like chasing qiongyao chasing points, and later found that data analysis can not directly improve performance, enthusiasm has been much colder

But if you really want to study on your own, I suggest:

According to the learning path I have always advocated, data analysis must first learn the basics and methods, and then learn the tools and skills, but many people just put the cart before the horse, I will say what should be learned according to the order of basics and tools:

1. Data analysis basis includes:

(1) Basic statistics

Mathematical statistics is one of the foundations of data analysis. Many people learn Python and Excel without a clear understanding of the concept of statistics, only to find it harder and harder to learn.

First, we need to understand some basic concepts of statistics. What is descriptive statistics? What is hypothesis testing? What is a normal distribution?

Then I will learn the data models in statistics, such as clustering and regression, which are essential for business analysis.

As for statistics, you can read the books “Statistics in Its simplest form”, “Naked Statistics” and “Introduction to Statistics”.

(2) The cultivation of data analysis thinking

Thinking is often ignored by many people, but in fact, as a data analyst, we should at least understand and learn the thinking patterns in data analysis, such as structured thinking, deductive reasoning, etc., which we can gradually cultivate in life.

Since data analysis is done by people, it is inevitable to be affected by personal thinking. To a large extent, data thinking can determine the direction and thinking of problem analysis. We suggest you to have a look at the following book:

(3) Data analysis model and method

Most of the time, we rely on the analysis model to do business analysis, so it is very necessary to learn some common data models, which are naturally developed based on our data analysis thinking.

For example, when I look at attrition analysis, I think of funnel model. For example, when I think of commodity correlation analysis, I must use the shopping basket model. For example, when I see membership analysis, I think of the RFM model

This part suggests everyone to see “analysis of data in depth”, “who says newbie won’t analysis of data” can also see, but simpler, as an entry book to see more appropriate

2. Data analysis tools and skills include:

(1) the SQL

Take a number of necessary skills, to master a certain database foundation, mainly to learn the syntax of SQL, I suggest you see “SQL Server: from entry to master”, “MYSQL will know will know” :

(2) the Excel

It mainly studies data cleaning, PivotTable and DAX functions. If you have the ability, you can learn VBA. However, it is not recommended to go too deep into business analysis.

(3) BI tools

The main tools used to do data analysis, such as Tableau, PowerBI, FineBI and so on, these tools have their own characteristics and applicable environment, you can refer to the following article: “This is probably the most recommended data analysis tool this year.”

(4) Python/R

Data analysis also requires mastery of at least one programming language, Python being the most versatile, but there are many people who prefer R, and the two are not that different for business analysis

Let’s start with this. This is the basics, so you can have a basic interview level

Finally, a word about career change

I used to be a big fan of data analytics, but ten years later, I’ve stopped telling people to get into the data analytics industry. The industry is so saturated with basic jobs that unless you’re a data analyst, it’s hard to get out of the red Sea

Changing careers is risky, and the more leverage you have, the more you can control it. You don’t have a lot of leverage yet, so don’t get chicken soup. There are a lot of people in this business who get chicken soup. Keeping a clear head is the key

Don’t look at a few we-media articles on the head, their hands to keep the cards to play, so I generally advocate business time to learn data analysis, after adequate preparation, no matter whether to advance or retreat will not panic